Extrinsic and intrinsic factors affecting the activity budget of alpine marmots (Marmota marmota)
Bibliographic record
Abstract
Abstract Extrinsic and intrinsic factors may influence the activity budget of wild animals, resulting in a variation in the time spent in different activities among populations or individuals of the same species. In this study, we examined how extrinsic and intrinsic factors affect the behaviour of the alpine marmot ( Marmota marmota ), a hibernating social rodent inhabiting high-elevation prairies in the European Alps. We collected behavioural observations during scan sampling sessions on marked individuals at two study sites with different environmental characteristics. We used Bayesian hierarchical multinomial regression models to analyse the influence of both intrinsic (sex and age-dominance status) and extrinsic (environmental and climatic variables) factors on the above-ground activity budget. Marmots spent most of their time above ground foraging, and were more likely to forage when it was cloudy. Extrinsic factors such as the site, period of the season (June, July–August, and August–September), and time of the day were all related to the probability of engaging in vigilance behaviour, which reaches its peak in early morning and late afternoon and during July, the second period included in the study. Social behaviours, such as affiliative and agonistic behaviours, were associated mostly with sex and age-dominance status, and yearlings were the more affiliative individuals compared to other status. Overall, our results suggest that in alpine marmots, intrinsic factors mostly regulate agonistic and affiliative behaviours, while extrinsic factors, with the unexpected exception of temperature, affect the probabilities of engaging in all types of behavioural categories.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".